{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ax1r8W0rU8d1"
   },
   "source": [
    "# 线性回归 - Linear Regreesion\n",
    "\n",
    "此Notebook是配合Andrew Ng \"Machine Leanring\"中[线性回归](https://github.com/loveunk/machine-learning-deep-learning-notes/blob/master/machine-learning/linear-regression.md)部分学习使用。\n",
    "\n",
    "测试用python版本为3.6\n",
    "* 机器学习路径：https://github.com/loveunk/machine-learning-deep-learning-notes/\n",
    "* 内容正文综合参考网络资源，使用中如果有疑问请联络：www.kaikai.ai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kgTnKLvOU8d2",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting seaborn\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from seaborn) (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (6.4.5)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.4->seaborn) (3.20.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(context=\"notebook\", style=\"whitegrid\", palette=\"dark\")\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nje4hmN0U8d6"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('ex1data1.txt', names=['population', 'profit']) # 读取数据并赋予列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a9p1GK2iU8d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1101</td>\n",
       "      <td>17.5920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5277</td>\n",
       "      <td>9.1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5186</td>\n",
       "      <td>13.6620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0032</td>\n",
       "      <td>11.8540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8598</td>\n",
       "      <td>6.8233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population   profit\n",
       "0      6.1101  17.5920\n",
       "1      5.5277   9.1302\n",
       "2      8.5186  13.6620\n",
       "3      7.0032  11.8540\n",
       "4      5.8598   6.8233"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # 显示数据前五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ly0Fs3unU8eA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97 entries, 0 to 96\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   population  97 non-null     float64\n",
      " 1   profit      97 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 打印df的class信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WsQ2uPs6muT7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.159800</td>\n",
       "      <td>5.839135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.869884</td>\n",
       "      <td>5.510262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.026900</td>\n",
       "      <td>-2.680700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.707700</td>\n",
       "      <td>1.986900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.589400</td>\n",
       "      <td>4.562300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.578100</td>\n",
       "      <td>7.046700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.203000</td>\n",
       "      <td>24.147000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       population     profit\n",
       "count   97.000000  97.000000\n",
       "mean     8.159800   5.839135\n",
       "std      3.869884   5.510262\n",
       "min      5.026900  -2.680700\n",
       "25%      5.707700   1.986900\n",
       "50%      6.589400   4.562300\n",
       "75%      8.578100   7.046700\n",
       "max     22.203000  24.147000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # 打印df的统计信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dtj0pJAOU8eE"
   },
   "source": [
    "***\n",
    "# 看下原始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wRWRxgtAU8eH"
   },
   "outputs": [],
   "source": [
    "def get_X(df): # 读取特征\n",
    "#     \"\"\"\n",
    "#     use concat to add intersect feature to avoid side effect\n",
    "#     not efficient for big dataset though\n",
    "#     \"\"\"\n",
    "    ones = pd.DataFrame({'ones': np.ones(len(df))})#ones是m行1列的dataframe\n",
    "    data = pd.concat([ones, df], axis=1)  # 合并数据，根据列合并\n",
    "    return data.iloc[:, :-1].as_matrix()  # 这个操作返回 ndarray,不是矩阵\n",
    "\n",
    "\n",
    "def get_y(df):#读取标签\n",
    "#     '''assume the last column is the target'''\n",
    "    return np.array(df.iloc[:, -1])#df.iloc[:, -1]是指df的最后一列\n",
    "\n",
    "\n",
    "def normalize_feature(df):\n",
    "#     \"\"\"Applies function along input axis(default 0) of DataFrame.\"\"\"\n",
    "    return df.apply(lambda column: (column - column.mean()) / column.std())#特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ON7EiaK7U8eE"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cmaizCgAA0VW5oEjlZQWqe7tJxYWOAZfWbNtWS5tPFXMLVbmgKIGtjB2GFMaJiTyrAAAQXaZpqnr1UmVnutXQ1CGvLyjLsuX1BdXQ1KGcLLeqVy8dt1cmxuermmAm+qwCAED0VS0p1bqaFaqYW6gub0DvH+xUlzegirmFuv+2FeP6igSX1caBkcwqWFgxPUGtBACkmqolpVq2uGTC1bISjsaB4cwqONLWPW5nFQAAYsc0zQn3xXp8R78JInJWwVDG+6wCAACiiXA0DvTNKjjc6pNt2wOO9c0qKC8rGLezCgAAiCbC0Tgw0WcVAAAQTfy1HCcm8qwCAIgHy7K0te6AXnj5XW2tO8AM4HGMguxxZKLOKgCAWGOR3YmFcDTOTMRZBQAQS32L7HZ0+jU5zyO3yyF/IBReZHddDaPz4w1DCgAAHAOL7E5MhCMAAI5hJIvsYvwgHAEAcAzDWWQ3ELRYZHecIRwBAHAMLLI7MRGOAAA4BhbZnZgIRwAAHAOL7E5M/DYBADgOFtmdeFjnCACAE2CR3YmFcAQAwDCwyO7EQeQFAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACIQDgCAACI4Eh0AwAgVViWpW07GtXc4lVBfoYqFxTJNPmOCYw3hCMAGIbazfWqWbtJu3Y3KxC05HKaKi8rUPXqpapaUpro5gGIIr7yAMAJ1G6u16rq9arb2aSsTJemTc1SVqZLdW83aVX1etVurk90EwFEEeEIAI7DsizVrN2kjk6/iouy5Ul3yjQNedKdKi7MVkeXXzVrN8myrEQ3FUCUEI4A4Di27WjUrt3NmpznkWEYA44ZhqH8XI927W7Wth2NCWohgGgjHAHAcTS3eBUIWnK7hi7RTHc7FAhaam7xxrllAGKFcAQAx1GQnyGX05Q/EBryeLc/JJfTVEF+RpxbBiBWCEcAcByVC4pUXlagw60+2bY94Jht22pp86m8rECVC4oS1EIA0ZZUU/n37dunhx9+WNu3b9c777yj0tJSrV+/fsB9Lr/8cr322muDHrthwwaVlZXFq6kAJgjTNFW9eqlWVa9XQ1OH8nM9Snc71O0PqaXNp5wst6pXL2W9I2AcSapw9M4772jjxo067bTTZFnWoG9pfc444wzdcMMNA26bMWNGPJoIYAKqWlKqdTUrwuscHWnrlstpqmJuIescAeNQUoWjqqoqnX/++ZKk6upq7dixY8j75eTk6PTTT49jywBMdFVLSrVscQkrZAMTQFKFIz5kACQz0zS1sGJ6opsBIMZSMo289tprOv3003Xqqadq5cqVev311xPdJAAAME4k1cjRcJx55pm6+OKLVVJSooMHD+rhhx/WF77wBT3++OOqrKwc9Xlt25bXG911Snw+34B/T2T0RT/6oh990Y++6Edf9KMv+o21LzIyhr/chmEfq+o5wfpqjo6erXY0r9erFStWqKysTA8++OConuvNN99UIBAY1WMBJJZl2dpV36bWjoAmZbtUXpor0zRO/EAAE8rChQuHfd+UGzk6WkZGhs4991y98MILYzqP0+nUnDlzotSqXj6fT3v37lVJSYk8Hk9Uz51q6It+9EW/sfbFy1v26Y4HXtXf97QoGLTkdJo6eXa+rrvybC1bPCsGLY4d3hf96It+9EW/ePZFyoejaDEMY0RDbiPh8Xhidu5UQ1/0oy/6jaYvajfX6+rv/kEdnX5NzvPI7XLIHwjpb+806+rv/kHralak5BR73hf96It+9EW/ePRFShZkR/J6vXr55Zd16qmnJropAOLEsizVrN2kjk6/iouy5Ul3yjQNedKdKi7MVkeXXzVrN8myrEQ3FUAKSqqRI5/Pp40bN0qSGhoa1NnZqeeff16SdNZZZ6m+vl4PPfSQLrjgAhUXF+vgwYN69NFHdejQId11112JbDqAONq2o1G7djdrcp5HhjGwvsgwDOXnerRrd7O27Whk6j2AEUuqcHT48GFdffXVA27r+/lnP/uZioqKFAwGdeedd6q1tVUej0eVlZW65ZZbVFFRkYgmA0iA5havAkFLbtfQH2HpboeOtHWruSW6M1ABTAxJFY5mzJihXbt2Hfc+Dz/8cJxaAyBZFeRnyOU05Q+E5El3Djre7Q/J5TRVkE+NBoCRS/maIwATT+WCIpWXFehwq2/QHoy2baulzafysgJVLihKUAsBpDLCEYCUY5qmqlcvVXamWw1NHfL6grIsW15fUA1NHcrJcqt69VK2JAIwKnxyAEhJVUtKta5mhSrmFqrLG9D7BzvV5Q2oYm6h7r8tNafxA0gOSVVzBAAjUbWkVMsWl2jbjkY1t3hVkJ+hygVFjBgBGBPCEYCUZpom0/UBRBVfrwAAACIQjgAAACIQjgAAACIQjgAAACIQjgAAACIQjgAAACIwlR/jjmVZrHsDABg1whHGldrN9apZu0m7djcrELTkcpoqLytQ9eqlrJgMABgWvk5j3KjdXK9V1etVt7NJWZkuTZuapaxMl+rebtKq6vWq3Vyf6CYCAFIA4QjjgmVZqlm7SR2dfhUXZcuT7pRpGvKkO1VcmK2OLr9q1m6SZVmJbipGwLIsba07oBdefldb6w7w+wMQF1xWw7iwbUejdu1u1uQ8jwzDGHDMMAzl53q0a3eztu1oZKuJFMElUgCJwsgRxoXmFq8CQUtu19B5P93tUCBoqbnFG+eWYTS4RAogkQhHGBcK8jPkcpryB0JDHu/2h+RymirIz4hzyzBSXCIFkGiEI4wLlQuKVF5WoMOtPtm2PeCYbdtqafOpvKxAlQuKEtRCDNdILpECQCwQjjAumKap6tVLlZ3pVkNTh7y+oCzLltcXVENTh3Ky3KpevZT1jlIAl0gBJBp/KTBuVC0p1bqaFaqYW6gub0DvH+xUlzegirmFuv+2FRTxpggukQJINGarYVypWlKqZYtLWCE7hfVdIq17u0nFhY4Bl9b6LpFWzC3kEimAmCEcYdwxTZPp+ims7xLpqur1amjqUH6uR+luh7r9IbW0+bhECiDm+HQBkHS4RAogkRg5ApCUuEQKIFEIRwCSFpdIASQCX8EAAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAiEI4AAAAisPEscByWZbErPABMMIQj4BhqN9erZu0m7drdrEDQkstpqrysQNWrl6pqSWmim4dxIDJ8Z2WYchl2opsEQIQjjFNjHfGp3VyvVdXr1dHp1+Q8j9wuh/yBkOrebtKq6vVaV7OCgIQxOTp8OxyGigvduumaTF103rxENw+Y0AhHGHfGOuJjWZZq1m5SR6dfxUXZMgxDkuRJd6q40KGGpg7VrN2kZYtLuMSGURkqfPu6A3p3b4e+fvMLSk93E76BBOKTHSnPsixtrTugF15+V/c//rpWVa9X3c4mZWW6NG1qlrIyXeERn9rN9Sc837Ydjdq1u1mT8zzhYNTHMAzl53q0a3eztu1ojNVLwjh2dPj2pDtlmoY86U5NyXersyuomrWbZFlWopsKTFiMHGFEkq1AOXKUyB/oUWt7t2xbmjUjV550p6SRj/g0t3gVCFpyu4b+3yPd7dCRtm41t3hj8powvp0ofOflusPhe2HF9AS1EpjYCEcYtmQrUD760oQn3dbhVp9s29b/NrRp1oxJys50SRo84nO8PzoF+RlyOU35A6FwwIrU7Q/J5TRVkJ8Rs9eG8etE4dvtcqi1PUD4BhKIy2oYlr4gMpbLVdE01KWJHqt3po/LYarHsvX+wY4Bj0l3OxQIWif8o1O5oEjlZQXhoBXJtm21tPlUXlagygVF0X1RmBAiw/dQ/AHCN5BohCOc0PFqJIoLs9XR5Y97jcRQlyYcDlOGIdky5Egz5feH5PUFw48Z7oiPaZqqXr1U2ZluNTR1yOsLyrJseX1BNTR1KCfLrerVSynGxqicKHwfafMTvoEE49MdJ5SMBcpDXZrISHco3e1QKGRJsmXZUqinN7CNdMSnakmp1tWsUMXcQnV5A3r/YKe6vAFVzC3U/bcxjR+jd6zw7esO6lCLX1mZLsI3kGBjqjn67Gc/q69+9atavHjxkMdfffVV/eQnP9HPfvazsTwNEiwZC5SHqgsyDENFU7L0vw1tCoYsGYZkGoa8vqBa2nwjHvGpWlKqZYtLkqoAHeNDX/juq+E70tYth8PQnJJs3XTNeYRvIMHGFI5ee+01XXrppcc83tLSotdff30sT4EkkIwFyn2XJureblJxoSM8opWT5dbM6Tna19Am0zTU1uGXy2mqYm7hqArHTdNkxhBi4ujw3btCdotOOWVWopsGTHhjnq129GWWSPv27VNmZuZYnwIJdqwgIvVfrqqYWxjXGom+SxOrqteroalD+bkepbsd6vaH1N4V0MziXF135f+n2SflMeKDpBUZvr1er3buPJLgFgGQRhGOnnnmGT3zzDPhn++77z796le/GnS/jo4O7dq1S+ecc87YWoiEO14QGc3lqmgZ6tLEWEaJAACQRhGOfD6fjhzp/3bT1dU15B/FjIwMffrTn9bq1avH1kIkhWQNItQFAQCibcTh6LLLLtNll10mSaqqqtK3v/1tnXfeeVFvGJJPsgYR6oIAANE0ppqj2traaLUDKYIgAgAY70YUjg4cOCBJmj59+oCfT6Tv/gAAAMluROGoqqpKhmFo+/btcrlc4Z9PZOfOnaNuIAAAQDyNKBzddtttkiSns3etm+9///vDCkcAAACpYkThKCcnRwsWLAgHoksuuSQmjQIAAEiUEU0zWrNmjV577bXwz+edd55efPHFqDcKAAAgUUYUjjIzM9Xe3h7+uaGhQV5v/PbTAgAAiLURXVarqKjQ/fffr8OHDys7O1uStHHjRjU3Nx/zMYZh6POf//yYGgkAABAvIwpHN998s2644Qb95Cc/kdQbfNavX6/169cf8zGEIwAAkEpGFI5mzZqlX/ziF/L7/Tp8+LCqqqr0rW99ixWyAQDAuDGqFbLdbremT5+uNWvW6Oyzz1ZxcXG02wUAAJAQY9o+ZM2aNeH/7urqUmNjoySpqKhImZmZY2sZAABAAowpHElSXV2dfvjDH+qvf/2rLMuS9MH+WwsX6v/8n/+jU089dcyNBAAAiJcxhaPt27fr8ssvl9Pp1Cc/+UmVlZVJknbv3q3f/e53WrlypR5//HFVVFREpbEAAACxNqZwdOedd6qwsFA///nPNWXKlAHHrrrqKv3bv/2b7rzzTj366KNjaiQAAEC8jGgRyKNt375dn/rUpwYFI0kqKCjQv/7rv+qNN94Yy1MAAADE1ZjCkWma6unpOeZxy7JkmmN6CgAAgLgaU3KprKzUk08+qYaGhkHHDhw4oJ///Oc644wzxvIUAAAAcTWmmqNvfOMb+sxnPqPly5frggsuUElJiSRpz549evHFF5WWlqbrrrsuGu0EAACIizGFo/nz5+vXv/617rzzTtXW1srn80mSPB6PPvzhD+uaa67RnDlzotJQABOPZVnatqNRzS1eFeRnqHJBEZfqAcTcqMNRIBDQH//4RxUXF2vt2rWyLEstLS2SpPz8/FF9gO3bt08PP/ywtm/frnfeeUelpaVD7tv261//Wg899JAOHDig2bNn69prr9VHPvKR0b4UAEmodnO9atZu0q7dzQoELbmcpsrLClS9eqmqlpQmunkAxrFRfwVzOp26+uqrtW3btt4TmaYKCgpUUFAw6m9277zzjjZu3KhZs2aF10w62u9+9zvdeOONWr58uR588EGdfvrpWrNmDbPigHGkdnO9VlWvV93OJmVlujRtapayMl2qe7tJq6rXq3ZzfaKbCGAcG3U4MgxDJSUlOnLkSNQaU1VVpY0bN+ruu+/WKaecMuR97r77bn3sYx/TNddco7PPPlvf+973dOqpp2rt2rVRaweAxLEsSzVrN6mj06/iomx50p0yTUOedKeKC7PV0eVXzdpN4RX5ASDaxnTxftWqVXryySdVXx+db3EnGnHav3+/9u7dq+XLlw+4/aKLLtKWLVsUCASi0g4AibNtR6N27W7W5DyPDMMYcMwwDOXnerRrd7O27WhMUAsBjHdj3j5k0qRJ+vjHP66zzjpLxcXFSk9PH3S/73znO2N5mrC+EDZ79uwBt5eVlSkYDGr//v3HvBwHIDU0t3gVCFpyu4b+eEp3O3SkrVvNLd44twzARDGmcPTEE0+E/3vLli1D3scwjKiFo7a2NklSTk7OgNv7fu47Phq2bcvrje6Hbd/svb5/T2T0RT/6ot9QfZGVYcrhMOTrDsiT7hz8mO6gHA5DWRlm1P+fTSTeF/3oi370Rb+x9kVGRsaw7zumcPT222+P5eFJJRgMaufOnTE59969e2Ny3lREX/SjL/pF9oXLsFVc6Na7ezs0Jd894NKabds61OLXnJJsuYwW7dwZvZrHZMH7oh990Y++6Dfavli4cOGw7zumcNTn73//uzZu3BheKXvGjBk655xzdPLJJ0fj9GG5ubmSpI6OjgH7ubW3tw84PhpOpzPqazL5fD7t3btXJSUl8ng8UT13qqEv+tEX/Y7VFzddk6mv3/yCjrQHlZfrltvlkD8Q0pF2vyblZuima87TKafMSmDLo4/3RT/6oh990S+efTGmcBQIBHTTTTfpv//7v2Xbdrig2rIs3XHHHfr4xz+uW2+9VS6XKyqNLS3tXdukvr4+/N99PzudTp100kmjPrdhGCMachsJj8cTs3OnGvqiH33R7+i+uOi8eUpPd4fXOWptD8jlNFUxr3Dcr3PE+6IffdGPvugXj74YUzj64Q9/qGeffVaXXXaZVq5cqZkzZ8owDO3bt0+PP/64/uu//ku5ubn69re/HZXGnnTSSSopKdHzzz+v888/P3z7hg0btHjx4qiFMACJV7WkVMsWl7BCNoC4G1M4+s1vfqOLL75YN91004DbS0tLdfPNN6uzs1O/+c1vhh2OfD6fNm7cKElqaGhQZ2ennn/+eUnSWWedpfz8fF111VW6/vrrNXPmTC1atEgbNmxQXV3dgOJwAOODaZpaWDE90c0AMMGMKRyFQiGddtppxzxeWVmpl156adjnO3z4sK6++uoBt/X9/LOf/UyLFi3SihUr5PP59OCDD+qBBx7Q7Nmzde+996qysnJ0LwIAACDCmMLR0qVLtWnTJl122WVDHv/jH/+oJUuWDPt8M2bM0K5du054v0svvVSXXnrpsM8LAAAwXGO6eH/11Vfrvffe05o1a7RlyxY1NDSooaFBf/rTn7R69WodOHBAV199tVpbWwf8AwAAkKzGNHJ00UUXSeqdyv/iiy8OOGbbtiTpYx/72KDHxWo9IQAAgLEaUzhavXr1oL2PAAAAUtmYwtFVV10VrXbgA5ZlMXUZAIAEisoK2YiO2s314UXvAkFLLqep8rKCcb/oHRALfNEAMFqEoyRRu7leq6rXq6PTr8l5nvB2CXVvN2lV9Xqtq1lBQAKGiS8aAMaCr1FJwLIs1azdpI5Ov4qLsuVJd8o0DXnSnSouzFZHl181azfJsqxENxVIen1fNOp2Nikr06VpU7OUlekKf9Go3Vyf6CYCSHKEoySwbUejdu1u1uQ8z6ACd8MwlJ/r0a7dzdq2ozFBLQRSA180AEQD4SgJNLd4FQhacruGvsqZ7nYoELTU3OKNc8uA1MIXDQDRQDhKAgX5GXI5TfkDoSGPd/tDcjlNFeSzIzNwPHzRABANhKMkULmgSOVlBTrc6gsvntnHtm21tPlUXlagygVFCWohkBr4ogEgGghHScA0TVWvXqrsTLcamjrk9QVlWba8vqAamjqUk+VW9eqlTEMGToAvGgCigb+2SaJqSanW1axQxdxCdXkDev9gp7q8AVXMLdT9tzGNHxgOvmgAiAbWOUoiVUtKtWxxCQvXAWPQ90Wjb52jI23dcjlNVcwtZJ0jAMNCOEoypmlqYcX0RDcDSGl80QAwFoQjAOMSXzQAjBZfowAAACIQjgAAACIQjgAAACIQjgAAACIQjgAAACIQjgAAACIwlR/ACVmWxZpBACYMwhGA46rdXB9ebToQtORymiovK2C1aQDjFl/9ABxT7eZ6raper7qdTcrKdGna1CxlZbpU93aTVlWvV+3m+kQ3EQCijnAEYEiWZalm7SZ1dPpVXJQtT7pTpmnIk+5UcWG2Orr8qlm7SZZlJbqpABBVhCMgSViWpa11B/TCy+9qa92BhIeObTsatWt3sybneWQYxoBjhmEoP9ejXbubtW1HY4JaCACxQc0RkASSsa6nucWrQNCS2zX0x0S626Ejbd1qbvHGuWUAEFuMHAEJlqx1PQX5GXI5TfkDoSGPd/tDcjlNFeRnxLllABBbhCMggZK5rqdyQZHKywp0uNUn27YHHLNtWy1tPpWXFahyQVHc2wYAsUQ4AmJguPVDyVzXY5qmqlcvVXamWw1NHfL6grIsW15fUA1NHcrJcqt69VLWOwIw7lBzBETZSOqHkr2up2pJqdbVrAi/niNt3XI5TVXMLWSdIwDjFuEIiKK++qGOTr8m53nkdjnkD4TC9UPralbo7Mr+y1CRdT2edOeg8yVDXU/VklItW1zCCtkAJgw+3YAoGX79UH/9TqrU9ZimqYUV03XhsjlaWDGdYARgXOMTDoiS4dYPbX+rKXw7dT0AkHz4xAWiZDj1Q4GgpeYjvgG399X1VMwtVJc3oPcPdqrLG1DF3ELdf9sK6noAIM6oOUpR7JI+UDL0x7Drh/I8kroHHKOuBwCSB+EoBSXjasqJlCz90Vc/VPd2k4oLHQMurfXVD1XMLdRp8wu1a9eRQY/vq+sBACQWX0tTTLKuppwoydQfw68fMk58MgBAwhCOUkgyr6acCMnYH8lSP5Rsm9gCQCrhsloKGclqyhPh8kyy9kei64eS5TIjAKQqwlEKSfbVlOMtmfsjUfVDw1mEkoAEAMfHZbUUwi7pA9EfAyXjZUYASEWEoxQSuZpylzeg9k6/vL6gpORaTTleYrm6dCrW7CTzJrYAkEq4rJZCTNPUheeWadPr/6tDh7tkGIZMQ3I60+R2OVSQn5G0qylblq2d77bqveY9mjEtLyo1OH2zw1ZVr1dDU4fycz1KdzvU7Q+ppc036tWlE1mzM5b1mpL5MiMApBLCUQqp3Vyv+5/YqnSXQ6ZpKBDskW3Z6vb3yLalVZ9ZmJT1JLWb6/Ufd2/UW38/KFum3K60QWFjtKEg2rvGx7tmJ/J179l/RE89t1N/H2UoS4VNbAEgFRCOUkRkPcnsmZMkSd7ukEIhS440Qy1tPr2wcbeuvWJxUo0c9YWN9o5uZXrSlJOdoWDIGhA2JI1ppCZas8OOrtnpuzTVW7PjUENTh2rWbtKyxSVR6eOXt+zTjx9+Xbt2N6ujK6COTr9M09S0wixNm5o14lA23EUoJ8plVwAYreT5K4rjOrqexDAMZXqcys12KzPDpcmTMkZUTxKPmprIsDG9MEvp7rRBBcL//h9/0JU3/HbMizhGY9f4eNbsvF7XrK/f/ILqdjYpM8OpUMiSLanHstR4sFNdvuCIC6nZxBYAooORoxQRzXqSeNXUDCds/O3vB5XhcapkxqRhjdTEcg+1eNXsWJatx55+Rx2dAc2YliNvd0jdgZCcjjSZhhQIWXr/YIeyZ08e8XpN0b7MCAATEeEoRUSrniSeNTUDw4Y96Lhl2QoELU2d7BrWIo6xDnXxqtnZ/laT9r3XpfxJvaExFLJk25JhSDIMOdJM+f0heX1BZXicIw5liV6EEgBSHZ+WKSIa09bjvQ7OidYh8vl7lyEYKohIvSM1gaCl5hZvXPZQi+XSAJGaj/gUDPWPUDkcpgxD6ntKw5AsWwr19P4eRhPKonGZEQAmKj4xU0Q06knivQ7OicJGV1dQToepYzW5LxTk56XHJdTFq2anIM8jp6M/NGakO5TudnwwgmTLtiXTkBxp5oRcvwoAEo1wlELGuqnpcGpq+kZqoiEybLx/sEvd/p4BYSNvkkcLyqeqpa37uCM1so24hbp4bBx72vxCzZqRqSNtftm2LcMwVDQlS2lphoJBS8FQj1yuNEmikBoAEoCaoxQzlnqSRKyD0xc2+tY58vq75HamhQuEJZ1wEceWVl9cFzeMdc2OaRr6/CUf0o8e3hl+3VkZLhUWZKrxUKcsy5DL6QiHMgqpASC+CEcpaLSbmiZqHZyqJaU667RCPfu7V5U9qWjQCtknml21te5A3ENdrDeOPbOiQHffcmF4naO+133Ooln6xEXzNfukPAqpASBBCEcTSKy22xjecxuaN2eS5s2brYyMgSHmRCM143Vxw2WLZ+mjHylnVhkAJBnC0QSTrOvgHG+kJpGhLtZiPUIFABg5wtEEdPRITf4kj2TYajnSra11B5Jy9CJZQx0AYPwhHE1QfSMWtZvr9e3bX0zIDvQjxeKGAIB4IBxNYPHegT4auAwFAIg1vnJPUPFeLRsAgFRBOJqg4r1aNgAAqYLLajFkWbZ2vtuq95r3DFrbJ9HitQN9LFmWRf0RACDqCEcxUru5PrwqtC1TbldaUhU6J2K17Giq3VwfnrmW7IXkAIDUwtfsGOgrdN6x65A86WkqmpIZ9R3kxypeO9DHQl//1u1sUlamS9OmZiVd/wIAUhfhKMoiC52nF2Yp3Z2WlIXO8dqBPtooJAcAxFpy/eUbB5K50NmyLG2tO6AXXn5XW+sOaNnikpjvQB9tydy/AIDxgZqjKBtY6GwPOp6oQufj1eg8/8TKlClsTpZCcorBAWD8IhxFWWShs9uVNuh4IgqdY7HYY6LCQTIUkk/EYnDCIICJhHAUZZE7yE+bkjngWCJ2kD+6RsfXHVKnNyBHmqnpU7O0r6FN19/6e62rWaGFp04f1h+8RIaDyP4tLnQMuLQWj/5NxVXFx2oihkEAExtf/aIsstD5/YNd6vb3JLTQua9GJz3doXf2tmj3vhbt2d+qd/YcVt3bB9XW4df2t5r0T1/4L3105RMnnOmV6JliiSwkn4jF4In+fQNAIhCOYqBvB/lTTi6Qr7tHjc1dCSt0bm7xqrMroPc/CBKSZEjq6bHD/0i2XK60E/7BS5Zw0Ne/8S4kn2jF4Mny+waAeOOyWoxULSnVWacV6un1W9Tc5pbL5dKiyhlaWDEtru3Iz0tXhzegYNCSYeiDMDSQbUsZHpcK8hxqaOpQzdpNWra4ZNDoy0jCQaw3h61aUqpli0tGVQcz2vqZZCkGj5dk+n0DQDwRjmLolT//r37y5NtqaPIrFLITUqvx+hsHFAz2frO3B+eiAU70By/ZwoFpmiP+ozyW+plkKAaPp2T7fQNAvKTcZbWnn35a5eXlg/750Y9+lOimDVC7uV5fv/kFvbO3Q1kZzoTUaliWpYd/8VcZ6r2UdiymYainpzdApbsdCgStIf/gRYaDoSR7OBhr/Uwqryo+Gqn++waA0UrZkaOHHnpI2dnZ4Z8LCwsT2JqB+ms1Apqa7z6qVuP4l66iaduORh1o6lCaw5BhGOrpsWVZg4ePDNOQI623Hcf7gxeNmWKWZWtr3YG4Twk/un6mr+0j+Z30FYOvql6vhqYO5ed6lO52qNsfUkubL2lXFR+tRM8MBIBESdlP8VNOOUWnn356+J9p0+Jby3M8fbUa+ZPSE1q42zf6k+52yLYlt7N3A9yjmiSnw1SGx3nC0Y+xzhR7va5ZF3/pV7rkil/o8994Vpdc8YthzZCLhmgVUyeqGDwRUnWLGQAYq5QdOUpmfbUaebkOBQI9g47Hq1aj97JImibnZajxYKeCPbYcaaacDlPBoBVev3tyXoa8vuCwRj/6wkFf3c6Rtm65nKZOLZ+qT1w0X8Fg7xYlR48Ivbxln75/X538AVsF+RlxXx8omvUzYykGTzXH+n1XzC1knSMA41bKhqMVK1boyJEjmj59uv71X/9VX/7yl5WWNnhF6kSIrNUYqtYnXrUakZdFZhbnqvFQp/z+kCxbMk3JsiWHw1QoZIVHP4bzB+/ocLBn/xE99dxO3XbvH4cscrYsS3c88Kq6fCHNnJ4b/j3F8zJjtIupR1MMnqomUhgEACkFw9GUKVN01VVX6bTTTpNhGKqtrdWPf/xjNTU16aabbhr1eW3bltcbnZGc8tJczSnJ045dh5Sf65Bl25LVN2PMVkurT6ecXKDy0tyoPeexXPOlM/X1m19Qe4dfxYVZ6rEkX3dQXl9Qk3Lc+voXz1LJjEkqyPPotPmFMk1j2G2aN2eSXt7Sph+t26yOzoDyJ6UrL/eDEaGdTbryht/o7lsuVG62W7vqDys3yylbGrQuTl6OWzvfPaQtf9kbs/qVvt/J3/7erGlTzcH1M3H8nfh8vgH/ThXz5kySNEmS1N3dHZVzpmpfxAJ90Y++6Edf9BtrX2RkDH9AwrCPnnaTgn7wgx/opz/9qV5++WVNnTp1xI9/8803FQgEotqm1+ua9f376uT1hZST5ZTLaSoQtNTeGVSGx6FvfbVCZ1YURPU5j9eWx55+R/ve61Kwx5IzzdSsGZn6/CUfGlMbLMvWVd97Ve/s7dDUfPegwHGoxa85Jdm67J9K9b27t6sgzy3THDyWZlm2mlv9+u7XT9fiypH//oYrmX4nAID4Wrhw4bDvm3IjR0NZvny5HnnkEe3cuXNU4UiSnE6n5syZE7U2zZsnFRYV6gc/2aT3Grvl9ffI6TB12vwiXXfl2Vq2eFbUnms4bVl56VJtf6tJzUd8A0aJxmLbjkY1NPk1dXLmkJeqpkx2qKHJr8ysKUpPdyoQtJSTkyHzqIJoX3dQnnRblRXlmjcvdjOf5s2TZs6cqTseeFV/39OSsN+Jz+fT3r17VVJSIo/HE5fnTFb0RT/6oh990Y++6BfPvhgX4SgaDMMY0ZDbcFx47smaURBSwM5Xp9caca1GtHdCX3JWdItnO72WQiFbnnTXkEHLk+5Sa3tA04tyVV46WdvfalRujga8Btu2daTdr4q5hVr8D701R7HcAf6i8+bpox8pT4r6GY/HE/X3XKqiL/rRF/3oi370Rb949MW4CEcbNmxQWlqa5s+fn+imDGKahirnFY34F5kKO6EPt8h56uQsXXfl2frKN9fr/YNdyp907PWB4vG6J1IxNQBg5FIuHH3pS1/SokWLVF5eLkl68cUX9atf/Uqf/exnNWXKlAS3Ljr6VnLu6PRrcp4n7tPeh2skiwR2d0/St75aoV8/f0Dv7j0y5JTwVHndAIDxLeXC0ezZs/XUU0+psbFRlmWppKRE3/rWt3T55ZcnumlREY2VnONlpCtGn1lRoJWXLtWu+rZBl7RS6XUDAMa3lAtH3/nOdxLdhJhKtZ3Qh7NIYF8N0ba6g+ruyQvXFkVKtdcNABi/Ui4cjWeWZWnL1v3q6AoMWcMjJedO6MdaJFCS7n/8dT3w861qaGxXKBhSRsZbmltWoE9cNF+zT8oL35cd4AEAyYJwlCT6CpHf3Nmktg6/Orr88qQ7NW1qtrIzXeH7JetO6EcXOddurte//8cftP2tJlmWLTPNkMthKC0tpFf+vE8vb9mnnCyXsjJdKi8r0CXL50VlBetYznQDAEwMhKMkEFmInD8pXV2+oLwfrGK9771WzZoxSdmZrpTZCb3v9fxvQ5ts2XK7TFm21B3okbfbpzTTkAwpEOpRVqZLdW83ac/+Vk2ZnKn3GttHvQN8KszwAwAkP75SJ9jRhcgZHpeKpmbJkWbKkKFQj6UDTe0psxN63+tpafXJti05HWkyTFOGIfWtxW7ZtpwOU4FAj2xbKi7MVkeXX5KUleEatAP8e43tcjnTdN7S2dq2o3HQ9iNSfyCr29mkrEyXpk3NCgevVdXrVbu5Pp7dAABIYcn5F3YCGaoQOSfLrZnFufJ4ekdQvL6QjnwwcnL/bck9nb3v9WRlOmXLUN8AkG33h6O+ny1bCvVY4YLrQ4e7dP2q/08VcwvV5Q3o/YOdOnzEq1DIlt8f0o8f+rMuueIX+ujKJwaEnaMDpifdKdM0Ppjp1hu8atZuGjJUAQBwNMJRgh2rEDkny60PleSrbGaecrPd+vZV5+j5J1YmdTCSel+PP9AjQ71DRT09tmy7958+vcHIlmlIjrTet2C626FA0NLsk/L0/BMr9fSDn9Y1XzpbbrdDTkdvrdGxRoNGMtMNAIATIRwlWOQq00czDEOmaSg706XFC09K2ktpkfbsP6LW9m41NHWox7IVCPbIHwjJighHhtG72azb7VCGp7f4OrLg2jRNVS4o0oub6xUI9JxwNGg4M90CQWtMM90sy9LWugN64eV3tbXuAKNQADCOUZCdYCNZZTpRhjsDrHZzve54YMsHl9A+qCsKWrKs3jDUx7Z7R4ymTc3+4OfBr3Mko0HD3cZktDP8KPQGgIkl+YciUpxl2dq2o/GYIw59q0xnZ7oHFSInQwF27eZ6fXTlE7rkil/o8994dsiaH2lg3c+sGblypPXOUHM504Y8r2EYx32dIxkN6guYh1t9Ay7fSf3Bq7ysYFQBk0JvAJh4CEcx9PKWfbrqe6/q02ueOW6w6FtlOrIQucsbSHgB9kiCQeRIT06WW7NmTJIn3TEoDE7KcSszw6lAsEd79h/R4SPeIV/n8S43SoMvw8UiYFLoDQATE5fVYqR2c72+fvMLOtLm09TJmfKku467ieqxVplO1IjRSPc6O3qkJzvTpaySfL29+7Bsf0hpaYZ6LFuT8zKUm+2W1xdUY3OnZp+Upw0/+4wcjoEjTCO93DicbUxGii1NAGBiIhzFQH+wCGhqvvuoEYdjb6J69CrTsWzfiULYSIPBUHU/3u6QgqEeOZ1pkmxZti2Hw5RhGMrMcGnalGy9f7BD299qGvS6R7qprRT9gMmWJgAwMRGOYqAvWORPSpehngHHEj3iMNzi4pEGg6FGekIhS7YtGbIV7LHldqYpI91xzHMcbTSjQdEMmLEu9AYAJCfCUQz0BYu8XIcCgZ5BxyNDQTz3AovcpmRynkdul+OYl/pGGgyGGulJM3tHnAIhS440U5Pz3Mc9x1ASebkxFWYSAgCij3AUA5HBwhjieF8o2LP/iD668om4TBEfaQ3RaILB0SM9/kCP0kxDtm1oxrRsuRz2Cc8xlHhdbhzqeUd6aQ8AkPr4VI+BvmBxpM1/zKnlUyZn6o4HtsRtivhIV5Ee7QywqiWl4RWuf3rnv+jH3/2oZhbnqrMrqG5/T1ItUzAcyTqTEAAQO4wcxUBfsLjyht/oUItPUyY75El3hUccsjNdknTCUZxzFs3S9reaElZcPJKan+NdHjy5dLL+4+6NeuvvB+X1d8ntTBvTLLJ4S7aZhACA2CIcxUjVklLdfcuF+t6PX1RDk1+t7YFwsLhk+Tzddu8fjzuKs/2tRi295BG939QRlUtuoy0uHk4wOFGRd9WSUp11WqGe/d2ryp5UpBnT8lIuXCTq0h4AIP4IRzG0bPEsTck5WwE7X51eKxws/vBK/XFHcYLBHh0+4lMgZGn61Cy5XQ61tnfrL3UH9PlvPKtHfnSxzv9w2YjaMpbi4uMFg+EWeZumoXlzJmnevNnKyGB2FwAgeaXOV/cUZZqGKhcU6cJlc7SwYrpM0zzu6s+2bevAwU7ZslVUkKlQj61397WoobFd7Z1+NTS261Nf+3/6nz/uHmE7or+KNCtIAwDGI8JRAhxvLzCvL6huf0jpbod6LFv73muVrzsk0zTkSDNlmoZa23z6wjeePW7R9lC7yEe7uHikRd4AAKQCLqslwPGmiDc2d8owpPxcj957v12hnt41ggJBS7Ztqy9LNR7q1L//xx/02vorBo32nKgGKFrFxSMr8p404vMDAJAIjBwlyLFGcYqmZMs0Db1/qFO+7pB6emz5A71T4CNZtq3tbzXqgSe3Drh9OJvF9tUQRV7qG42RbA4LAECqIBwlUOSaQI/95z/rm2s+rGCwR4Y06HKbpPCokWkacjpMWZatB3++NVzTE+8aoONdHuwr8i4vK2AFaQBASiEcJVjfKM4F55Tq6ed2qrMroFnFuUpLG/pXYxiS02HKMEyZaYYONHWEa3riXQMUiyJvAAASjb9aMWZZtrbtaBxQGD2UyGCTk52ukhmTNFSmcDhMpZmGQj2W0t0OSUZ44cbh1AAFglZUd5FnBWkAwHhDQXYMvbxln77341fV0ORXKGQfdyHHo4NNTpZbxUU52n+gfdB5AyFLaaahyZMyZNt2uKYnUbvIs4I0AGA84a9XjNRurtfXb35B7+ztUFaG84R7pw1V3Dx1cqYyPE5FXiGzbVuedIdmFueqOxAaUNOTyBqgaBV5AwCQaPwFi4H+wuiApua7h1UYfaxgM2Nazgc1RpLbnabSk/I0oyhH7Z3+QTU91AABADB2/JWMgb76ofxJ6cMujD5WsEkzDWVmuJTudig3y632zsBxa3qoAQIAYGyoOYqBvvqhvFyHAoGeQccHLo7Yry/Y9C3geKStWy6nqX+omK5//+oS5eV6hlXTQw0QAACjRziKgcj6IWOI48crjI5WsOmrAbIsS9t2NOoPr9QTkgAAGAbCUQz01Q/V7WxSXk7agGN9hdEVcwuPWRjdF2yO1hd0hhuaTrSNCAAAGIxwFAN99UNX3vAbHWrxacpkhzzpLnX7Q2pp842qMHqkQadvG5GOTr8m53nkdjnkD4TCs+XW1VB/BADAULi+EiNVS0p19y0Xak5Jtjq9wTEVRg9nv7RI8d5GBACA8YSRoxhatniWpuScrYCdr06vNaqan6ODTt/st96g41BDU4dq1m7SssUl4fOOZBuRoS7fAQAwkRGOYsw0DVXOK1JGxuhWpR5N0BnONiJDzZYDAABcVkt6o9kvbajVtiPFahsRAADGA8JRkhtN0EnkNiIAAKQ6wlEcWJatrXUH9MLL72pr3YERFUKPJuiwjQgAAKNHzVGMvV7XrH+//Vd6d++RAVPwh7vidV/QWVW9Xg1NHcrP9Sjd7TjhsgDHWm27Ym4h6xwBAHAchKMYennLPn3/vjr5A7YK8jPCaw39pe6A/umL/6XsTJdM0zzhmkWjDTpsIwIAwMgRjmLEsizd8cCr6vKFNHN6rtLSelfKDoYseX1BBYI9MgypvLTghIszWpal3Ox0XXvF2Tp02KvWtm7JkCblpMvv79HWugPHHXliuj4AAMNHOIqRbTsa9fc9LcrNcoan4Nu2rcZDnerpseVypikYstTa3i2Hw1ReTrqOtHcPWrMocmXszq6AvN1B9Vi2bEuyZSvNNDUpx63T5hdxuQwAgCjg+kqMNLd4FfygxqiPtzukbn9IDkfvbaGQpf0H2rVnf6vq//eIOjr92v5Wo7btaJQ0cGVswzTU6Q0oEOxRMGgp1GPJkCHLsnWkvVt/qTsw5GrZAABgZAhHMVKQnyGn01Qg2D8zLRSyZNu9l8kCgR7Zdu8ikU6HKdM05Q/06HCrT/+zafeglbFbWn2ybEnqXwjSsm05HIZsq/fc7Z3dbAsCAMAYEY5ipHJBkU6ena/2zmB4Cr7DYcoweuuObPUGI0eaIcMwekNSminblp557m1tffNAeGVsX3dIfn9IpmHItm0ZRm9E6j2vIYfDVHcgpIx0V3i1bAAAMDqEoxgxTVPXXXm2MjwOvX+wS15fUOmuNKWZvQFIkpwOU4qoRwr12Ep3O3SgqV1//mtDeGXsUI8ly+69a+RSR7atcFiybSktzRi0WjYAABgZwlEMLVs8S9/6aoVOOblAXd6AGg91ye3unbWWZvaOGNm2LcuyFQxaSkszNH1qloKh3gTUtzK2I82U+UEAitxezTD0wTl6/7u30JttQQAAGAtmq8XYmRUFWnnpUu2qb1Nzi1eHDnfpG997Xt7ukALBnnCw8XgcKpqSJUeaKZezR4sqZ6i8rEB1bzepuDBbbrdDvu6QDKO3CFuSTMOQZCsUsuVJd8jbHdBp84rYFgQAgDEgHMWBaRrhtYYsy9LPntqu7TsbdVJujkI9thwOUxnpvb+KhqYOVcwt1MKKaYNWxn7f36HeaqUPzmsYCoVsGWZvPVNudvoxtwWxLIvFIAEAGAbCUZxFbgdypL07vB2Ir3vwdiBHr4ydlemW1xcY8TpHkWslRW5hwrpIAAAMRjhKgJFsB3L0FiD5eemSbaj5SJcOt/g0OS+jt8bIsNVypHvQatl9ayV1dPo1Oc8T3sLkeCtyAwAwkRGOEmQk+54dbwuQ2s31+vbtLw45KrRsccmAtZL6Vur2pDtVXOhQQ1PHoBW5AQCY6AhHCTTWfc9ONCp03ZWLw2slGZHT3NQ7yy0/1xNeF4n91wAA6MVwQYo6egVtT7pTpml8MCqUrY4uvx78+Vb5Az1yu4bOwOluB+siAQBwFEaOUtS2HY0nHBU60NQhQ5I/EJIn3TnoHN3+0KjXRWL2GwBgvCIcJRnLsrT1zQP6818bJEmLKmdoYcW0QcGjucUbXkF7KOluhyRD04qydKCpQ8WFjgEhyrZttbT5VDG3cMTrIjH7DQAwnhGOkkjt5nr9+3/8QX/7+8HwhrVOh6kF5VN1+7cvGBA8CvIzwitop7sd8naHFApZ4TWT+kaFrrxsoe54YEt4raR0d++xo5cNGEkbmf0GABjPuA6SJGo31+uz1zyjN/7WqGDIktNhyukwFeqx9MbfGnX51c+odnN9+P6VC4pUXlag9w926p09Ldq9r0V732vV7n0temdPixoPdaq8rEBXfmah1tWsUMXcQnV5A3r/YKe6vAFVzC3U/beNLMgMp86pZu0mWZYViy4CACAuGDlKApZl6ba1m3TosFcyJLczLbyJmmkaCgYtNbd06bZ7/xiedm+api48t0wvb9mrUI8lpyNNaWmSZUmd3oAcab3H+xaTHO6yAccznDonZr8BAFIdI0dJYNuORu14u0mW3RtyIneXNQxDDocpy5Z2vH1Q23Y0SuoNVC9s3K2sTJcyM5ySbIV6bEm2MjOcysp06YWNu8OjOH3LBly4bI4WVkwfVfH0cOqcmP0GAEh1jBwlgeYWr/yB/k1oI1mWLcuyZNu2/MGecPDoG8WZNjVLnnSnvL6gQj2WHGmmMjy9P0d7FCeyzinas98AAEgWhKMkUJCfIbcrTYahcEDq6bEUDPWGIvuDvWa7vAHt2X9E0uBRnAzPwLCS7nboSFv3gFGcsU6/76tzqnu7Kaqz3wAASCaEoyRQuaBIp5RP1cZX9ykQDCnNNBUMDS5qtm1bP1r3J51cOnnEozjRmH4fuWlutGa/AQCQbPgrFieWZWlr3QG98PK72lp3YMCMrtrNe3SgsUNWjy3L0pDByOkwVTJjkjq9AdWs3aTT5heqvKxAh1t9svuGlj7QN4pTXlagygVF4en3dTublJXp0rSpWcrKdIWn30fOgjuRvk1zozH7DQCAZMTIURy8vGWffvzw60OO2mzb8b5u/NFL8gdCkiHJHvx4T7pDxUXZSnOkyeVI05s7m7RtR+OwRnEkRX3z2WjNfgMAIBkRjmLs9bpm/ejhnersCg5aNHHl159WS6tPgWCPXM40maahUI+l4AcLQBpGb4iZPjVTTc1d6vaHZNu9Rdpf+eZv9cPv/KPW1awIXy470tYtl9NUxdzC8OWyrXUHYjL9fqyb5gIAkKwIRzFkWbYee/oddXQGNGNazoBRm+lT0/TmroMKhWylu9JkpvWOuqSZpkKywgNIgUBI+xraZduSw2F+cAnN1r732sIrUj//xMpjjuIMZ/r90YXbAABMZISjGNr+VpP2vdel/EmDR218/h71lR1FXkkzTUOGaci2emephXpsmaZ6Z7NJCvTY8qQ7NWtG7oBLYscaxRlO4bZk6509h7k8BgCAKMiOqeYjPgVDQ4/ahEJWeE0jyxpYaOR0mAPWO0ozDdm2FAhZSjMNTZuaPeiS2LH0Tb8fqnC7vdOv3fuO6Ehbt269+xVdcsUv9NGVT4yoQBsAgPGGcBRDBXkeOR29ozZS7yyyLl9QbR1+hT5Yw0iSLNseEFzS0kw50vp/NT2WJcuy5El3aNaMScrOdEka3orUfdPvszPdamjqkNcXlGXZam7xqn7fEfX0WCqakqnphdmjnsEGAMB4wmW1GDptfqFmzcjU3vd86umx1RhRVN234KNpGnKkmQoGLTkcpgzZ6rFshXp6Z7VleJzKyXYr0+MatNDjcFek7pt+H1m43druU1qaoVnFucrJTpc0thls8TbWBS0BADiWlAxHu3fv1q233qpt27YpMzNTF198sa655hq5XK5EN20A0zT0+Us+pO/dW6f6/22VDPWOCBlSqKf3spphSE6nqbS03uLr3v3RemV4nDJNUx2dARXkDQxAI12ROnL6/Zat+/Ufd7+ivEnpyvAM7LNU2EA2GgtaAgBwLCn3VbutrU2f+9znFAwGdc899+jaa6/Vr371K9XU1CS6aUNauGCyiqZkKS3NlGlIPVbvJbQMj1Nls/KVk+WWy+lQRnqaLMuWYUjp7jSVzcrT9MJs2batzq6A9uxvDV8S8/qCamjqGPGK1H3T7z80e7JkGEp3Dy7QlpJ7A9loLmgJAMBQUm7k6Be/+IW6urp07733atKkSZKknp4e3XLLLVq1apUKCwsT28Cj7Kpv06EWr+aU5H0w+6x/c1ipdySpo8uvSTnpCllSUUGmMjP6R3NmnzRJe/a3yjAMdXb5h1zLaKRSdQNZy7KivqAlAABHS7m/IK+88ooWL14cDkaStHz5clmWpc2bNyeuYcfQ2hFQ8IN1hjI8TuVkuQfUDqW7HfL5Qmpq7tL0qVkDgpHUe5mraEqWMjOc+v4N5+ux//xnPf3gp/X8EytHfQnpeDPYjt56JJls29E47AUtAQAYrZQLR/X19SotHRgKcnJyNGXKFNXXJ98llUnZLjmd/TPWjtbtD8k0DVmWfdyFGoNBS1MmZ+rCZXO0sGL6mEZGjjWDbbSX6+JlOAtaJuvlQABA6ki5y2rt7e3KyckZdHtubq7a2tpGfV7btuX1RvePqs/nU3lprubMmqSd77Zo2lRzwIiHbdtqafVp1owcNR7qkq87MORlLl93UA6HoawMM2ptPLuySHd99wLd8cCr+vueFrW0+eR0mDrlQwW67sqzdXZlUVT7w+fzDfj3aGRlmHI4jLj2UyxEoy/GC/qiH33Rj77oR1/0G2tfZGQMv1Qk5cJRrASDQe3cuTPq5zVNQ5+6aIa+f1+L9h9oU06WUy6nqUDQUntnUBkeh75wSYl+9uy7endvh6bkuwcFqEMtfs0pyZbLaNHOnUei1rbCSVLN9adqV32bWjsCmpTtUnlprkzTG5O+kKS9e/eO+rEuw1ZxoTvu/RQrY+mL8Ya+6Edf9KMv+tEX/UbbFwsXLhz2fVMuHOXk5Kijo2PQ7W1tbcrNzR31eZ1Op+bMmTOWpg3i8/m0d+9eXfpP/6CZM2eGR2m8/h45HaZOm1+k6648W8sWz1JJySx9/eYXdKQ9qLxcd3iD2iPtfk3KzdBN15ynU06ZFdX29TnllJicdoC+vigpKZHH4xn1eW66JjNh/RQt0eqL8YC+6Edf9KMv+tEX/eLZFykXjkpLSwfVFnV0dOjQoUODapFGwjCMEQ25jYTH49FF583TRz9SfsyFCy86b57S093h9Xta2wO9s9LmjX5WWjLyeDxj6ufx1E9j7YvxhL7oR1/0oy/60Rf94tEXKReOzjnnHN1///0Dao+ef/55maapJUuWJLh1x9e3ztCxRC7UyMrPx0Y/AQBiKeXC0ac//Wk9/vjjWr16tVatWqWmpibdfvvt+vSnP510axyNxokCFHrRTwCAWEm5r9q5ubn66U9/qrS0NK1evVp33HGHPvnJT6q6ujrRTQMAAONAyo0cSVJZWZkee+yxRDcDAACMQyk3cgQAABBLKTlylKosy6KIGACAJEc4ipPazfXh6eeBoCWX01R5WUHKTT8HAGC8Y9giDl7esk+rqterbmeTsjJdmjY1S1mZLtW93aRV1etVuzn59oQDAGCiIhzFmGXZuuOBV9XR6VdxUbY86U6ZpiFPulPFhdnq6PKrZu0mWZaV6KYCAAARjmJuV32b/r6nRZPzPAP2ApN6V+XOz/Vo1+5mbdvRmKAWAgCASISjGGvtCCgYtOR2DV3ele52KBC01NySvLvIAwAwkRCOYmxStktOpyl/IDTk8W5/SC6nqYJ89swBACAZEI5irLw0VyfPztfhVp9s2x5wzLZttbT5VF5WoMoFRQlqIQAAiEQ4ijHTNHTdlWcrO9OthqYOeX1BWZYtry+ohqYO5WS5Vb16KesdAQCQJPiLHAfLFs/SupoVqphbqC5vQO8f7NSRNp+KC7P1jSsWa9nikkQ3EQAAfIBwFCdVS0r1/BMr9c01H9a0wizJlhoaO3TbvX/UR1c+wVpHAAAkCcJRHL28Za/ueGCLDjR2KD/Po+mF2SwGCQBAkiEcxYllWapZuymhi0FalqWtdQf0wsvvamvdARaeBABgCOytFifbdjRq1+7mYS0GubBietSfn73dAAAYHkaO4qS5xatAghaDrN1cz95uAAAME+EoTgryM+RKwGKQyXA5DwCAVEI4ipPKBUUqLyuI+2KQI7mcBwAACEdxY5qmqlcvjftikIm8nAcAQCoiHMVR1ZLSQYtBdnkDqphbqPtvWxGTwuhEXc4DACBVMVstzqqWlGrZ4hJt29Go5havCvIzVLmgKGbbh/Rdzqt7u0nFhY4Bl9b6LudVzC1kbzcAAD5AOEoA0zRjMl3/WM9VvXqpVlWvV0NTh/JzPUp3O9TtD6mlzcfebgAAHIW/iBNAIi7nAQCQqhg5miDifTkPAIBURTiaQOJ5OQ8AgFTFsAEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEwhEAAEAEtg+JIcuytfPdVr3XvEczpuWxlxkAACmAcBQjtZvr9R93b9Rbfz8oW6bcrjSVlxWoevVSVS0pTXTzAADAMTCMEQO1m+u1qnq9duw6JE96moqmZCor06W6t5u0qnq9ajfXJ7qJAADgGAhHUWZZlmrWblJHp1/TC7OU7k6TaRrypDtVXJitji6/atZukmVZiW4qAAAYAuEoyrbtaNSu3c2anOeRYRgDjhmGofxcj3btbta2HY0JaiEAADgewlGUNbd4FQhacruGLudKdzsUCFpqbvHGuWUAAGA4CEdRVpCfIZfTlD8QGvJ4tz8kl9NUQX5GnFsGAACGg3AUZZULilReVqDDrT7Ztj3gmG3bamnzqbysQJULihLUQgAAcDyEoygzTVPVq5cqO9Ot9w92qdvfI8uy5fUF1dDUoZwst6pXL2W9IwAAkhR/oWOgakmp1tWs0CknF8jX3aPG5i51eQOqmFuo+29bwTpHAAAkMRaBjJGqJaU667RCPfu7V5U9qYgVsgEASBGEoxgyTUPz5kzSvHmzlZFBATYAAKmAYQwAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhCMAAIAIhm3bdqIbkWh//etfZdu2XC5XVM9r27aCwaCcTqcMw4jquVMNfdGPvuhHX/SjL/rRF/3oi35j7QuXy6Xy8vJh3dcx4rOPQ7F6wxmGEfXAlaroi370RT/6oh990Y++6Edf9ItnXzByBAAAEIGaIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAiEIwAAgAhsPDsKTz/9tL75zW8Ouv2KK67Q9ddff8zH2batBx98UD//+c/V0tKiefPm6Zvf/KZOP/30GLY2ti6//HK99tprQx77z//8T33sYx8b8lhVVZUaGhoG3V5XVye32x3VNsbKvn379PDDD2v79u165513VFpaqvXr1w+6369//Ws99NBDOnDggGbPnq1rr71WH/nIR054/qamJt16663atGmTnE6nLrjgAn3zm99UVlZWLF7OmJyoLzo7O/Xoo49q48aN2rt3r1wulyoqKnTttdeecJfsP//5z/rsZz876PaLLrpId955Z9Rfy1gN531xrP9vNmzYoLKysuOefzy9L9577z2dd955Qz7W5XLpzTffPOa5U+l98dxzz+k3v/mN/va3v6m9vV2zZs3S5Zdfrk984hMDNj6fCJ8VJ+qLZPmsIByNwUMPPaTs7Ozwz4WFhce9/4MPPqi7775b119/vcrLy/Xkk0/qi1/8ov77v/9bJ510UqybGxM333yzOjs7B9z205/+VL///e+1ePHi4z72wgsv1Be/+MUBt6XS7tPvvPOONm7cqNNOO02WZWmoPZx/97vf6cYbb9RXvvIVnX322dqwYYPWrFmjJ5988rihOBgM6stf/rIk6Y477lB3d7d+8IMf6LrrrtO6deti9ZJG7UR9ceDAAf3yl7/UJz7xCV1zzTXy+/165JFH9KlPfUpPPfXUCQOBJN12220qLS0N/5yXlxf11xENw3lfSNIZZ5yhG264YcBtM2bMOO65x9v7YurUqfrlL3854DbbtvXlL39ZZ5999rCeIxXeF4899piKi4tVXV2tvLw8/elPf9KNN96oxsZGrVmzRtLE+aw4UV8kzWeFjRF76qmn7JNPPtk+fPjwsB/T3d1tn3HGGfYdd9wRvs3v99sf+chH7JtvvjkGrUycqqoq+4orrjjufT7ykY/Yt9xyS5xaFBs9PT3h/77hhhvsj33sY4Pu84//+I/2N77xjQG3fepTn7K//OUvH/fcv/3tb+3y8nJ79+7d4dv++Mc/2ieffLK9ffv2MbY8+k7UF11dXbbX6x1wW2dnp33WWWfZ3/ve94577ldffdU++eST7bq6uug1OIaG875YuXKlfeWVV4743OPtfTGUvt/3hg0bhnW/VHhfDPW34jvf+Y59xhlnhPtoonxWnKgvkuWzgpqjOPnrX/+qzs5OLV++PHyby+XSBRdcoFdeeSWBLYuuv/71r3rvvff08Y9/PNFNiTnTPP7/Pvv379fevXsH/M6l3iHeLVu2KBAIHPOxr7zyisrLywd8+1myZIkmTZqkjRs3jq3hMXCivsjIyJDH4xlwW2ZmpmbOnKmDBw/Gsmlxd6K+GIvx9r4Yyvr165WVlaWqqqoYtCgx8vPzB902b948dXZ2yuv1TqjPihP1RbJ8VhCOxmDFihWaN2+ezjvvPK1bt049PT3HvG99fb0kDXgDS1JZWZkOHDig7u7umLY1XtavX6+MjIxj1hFE+u1vf6sFCxaosrJSV1xxhXbt2hWHFsZP3+989uzZA24vKytTMBjU/v37j/vYo98rhmFo9uzZ4fOmuvb29nAdynBceeWVmjdvns455xz94Ac/SPn/Z1577TWdfvrpOvXUU7Vy5Uq9/vrrJ3zMeH9fBINB/f73v9cFF1ww7NrDVH1fbN26VYWFhcrKyprwnxWRfTGURHxWUHM0ClOmTNFVV12l0047TYZhqLa2Vj/+8Y/V1NSkm266acjHtLe3y+VyDfofPicnR7Ztq62tTenp6fFofsyEQiE999xzqqqqUkZGxnHvW1VVpYqKCk2fPl379+/X/fffr8suu0zPPvtsytZfHa2trU1S7+84Ut/PfceH0t7ePqCerU9ubu5xH5dKfvjDH8owDP3bv/3bce+XnZ2tL3/5yzrzzDPldrv16quv6pFHHlF9fX1S1lQMx5lnnqmLL75YJSUlOnjwoB5++GF94Qtf0OOPP67KyspjPm68vy9eeeUVtba2asWKFSe8byq/L/7yl79ow4YN4ZqzifxZcXRfDCURnxWEo1H48Ic/rA9/+MPhn5cuXSq3262f/vSn+spXvqKpU6cmsHWJs3nzZrW0tAzrg+073/lO+L//4R/+QUuWLNHy5cv18MMP67vf/W4MW4lk8NRTT+lXv/qVampqVFRUdNz7zp8/X/Pnzw//vHjxYk2dOlXf+973VFdXp4qKilg3N+q+/vWvD/h52bJlWrFihX7yk5/owQcfTFCrEu+3v/2tCgoKTjiZQ0rd90VjY6OuvfZaLVq0aMiZVRPJcPoiUZ8VXFaLkuXLl6unp0c7d+4c8nhOTo4CgYD8fv+A29vb22UYhnJzc+PRzJhav369Jk2apKVLl474sVOnTtXChQv1t7/9LQYtS4y+32lHR8eA29vb2wccH0pOTs6gWYBS7zfIVH+vbNy4UTfddJO+9rWv6V/+5V9GdY6+2owdO3ZEs2kJk5GRoXPPPfeE7//x/L7o6urSSy+9pOXLlystLW1U50j290V7e7uuuOIKTZo0Sffcc0+4JmsiflYcqy8iJfKzgnAUJ33XSvfs2TPg9vr6ek2fPj3lL6l1d3frf/7nf/TRj35UTqcz0c1JCn2/86Ov+9fX18vpdB738mFpaemgx9m2rT179gz7unsyeuONN3T11Vfrn//5n3X11VcnujkpZ7y+LyTpD3/4g7q7u8ftZI7u7m6tWrVKHR0dg5aBmWifFcfriz6J/qwgHEXJhg0blJaWNmBIL9IZZ5yhrKwsPffcc+Hb+ooPzznnnHg1M2Zqa2vl9XpH/cHW1NSkrVu36tRTT41yyxLnpJNOUklJiZ5//vkBt2/YsEGLFy8+7ppO55xzjt5++23t3bs3fNuWLVvU2tqqc889N1ZNjql3331Xq1at0tlnn61bbrllTOf63e9+J0nj5v3i9Xr18ssvn/D1jMf3RZ/169dr5syZOu2000Z9jmR9X4RCIV1zzTWqr6/XQw89NGhNvIn0WXGivpCS47OCmqNR+NKXvqRFixaFV+t88cUX9atf/Uqf/exnNWXKFEnS5z73OR04cEB/+MMfJElut1urVq3SPffco/z8fJ188sn6r//6L7W2tupLX/pSwl5LtPz2t7/V9OnTtXDhwkHHju6L9evX66WXXtK5556rqVOnav/+/XrggQeUlpamL3zhC/Fu+qj5fL7wVNmGhgZ1dnaGP9zOOuss5efn66qrrtL111+vmTNnatGiRdqwYYPq6ur0xBNPhM/T0NCgCy64QF/72tfCC8JdeOGFWrduna666ip94xvfkM/n0+23365ly5YlZS3FifrCtm196Utfktvt1uc+97kBQ9xZWVmaM2dO+LFH98X111+vWbNmaf78+eEiy8cee0znn39+0v0RlE7cF31/FC644AIVFxfr4MGDevTRR3Xo0CHddddd4fNMhPdF37TulpYWbdmyRVdcccWQ50n198Utt9yil156SdXV1ers7NQbb7wRPjZ//ny5XK4J81lxor7o6OhIis8KwtEozJ49W0899ZQaGxtlWZZKSkr0rW99S5dffnn4PpZlDZraf8UVV8i2bT3yyCPh7UMefvjhlJ+d1dbWpj/+8Y/63Oc+N2Ap/D5H98WMGTN08OBBff/731dHR4eys7N19tln6+tf/3pK9cXhw4cHDff2/fyzn/1MixYt0ooVK+Tz+fTggw/qgQce0OzZs3XvvfcOmJFk27Z6enoGrB7sdDr10EMP6dZbb9U3vvENORwOXXDBBfrWt74Vnxc3QifqC6m3+FKSPv/5zw+431lnnaXHH39c0tB98aEPfUi//e1v9cgjjygYDKq4uFhf+cpXdOWVV8bq5YzJifqiqKhIwWBQd955p1pbW+XxeFRZWalbbrllwB+zifC+WLRokaTeLSVCodAxR55T/X2xefNmSVJNTc2gYy+++KJmzJgxYT4rTtQXDQ0NSfFZYdj2Mda2BwAAmICoOQIAAIhAOAIAAIhAOAIAAIhAOAIAAIhAOAIAAIhAOAIAAIhAOAIAAIhAOAIAAIhAOAIwoVx++eUDVrOPhnvuuSe8nRCA1Ec4AoBh8Pl8uueee/TnP/850U0BEGOEIwAYBp/Pp3vvvVevvfbaoGNf/epXVVdXl4BWAYgFNp4FgDFyOBxyOPg4BcYLRo4ARE1f7c3u3bt19dVX64wzztCiRYt06623yu/3h+8XCoW0du1anX/++VqwYIGqqqr0n//5nwoEAgPOV1VVpVWrVmnTpk26+OKLdeqpp+qiiy7S73//+yGf92hPP/20ysvL9d577x2zzYFAQHfddZcuueQSLVy4UKeffrouu+wyvfrqq+H7vPfee1q8eLEk6d5771V5ebnKy8t1zz33HPP5R/oa//KXv+iTn/ykTj31VJ133nl69tlnj9PTAGKJcAQg6q655hr5/X5dd911Ouecc/T444/rxhtvDB//zne+o7vvvlvz58/XN7/5TZ155plat26drr322kHn2rt3r6699lqdc845uu6665SWlqarr75amzdvjkpbOzs79etf/1pnnXWWrr/+eq1Zs0YtLS368pe/rJ07d0qS8vPz9d3vfleSdMEFF+j222/X7bffrgsuuOCY5x3Ja9y3b5+uvvpqLVmyRNXV1crNzVV1dbXeeeedqLxGACPDODCAqJsxY4buu+8+SdJnPvMZZWVl6ec//7m++MUvSpKeeeYZXXrppbr11lvD98nPz9cjjzyiV199VWeffXb4XHv37tU999yjf/zHf5QkffKTn9RHP/pR/ehHP9KSJUvG3Nbc3FzV1tbK5XKFb/vXf/1XLV++XI8//ri+//3vKyMjQxdeeKG++93vqry8XBdffPFxz/n222+P6DXu2bNHTz75pP7hH/5BkrR8+XKde+65evrpp3XDDTeM+TUCGBlGjgBE3Wc+85kBP69cuVKS9Morr2jjxo2SpC984QsD7tMXnPqO95k6deqAEZqsrCz98z//s9566y0dOnRozG1NS0sLByPLstTa2qpQKKQFCxborbfeGtU5R/oa58yZEw5GUu9I1ezZs7V///5RPT+AsWHkCEDUzZo1a8DPM2fOlGma4dof0zQ1c+bMAfeZMmWKcnJy1NDQMOhchmEMuK2kpESS1NDQoClTpoy5vc8884weeeQR7dmzR8FgMHz7jBkzRnW+hoaGEb3GadOmDTpHbm6u2traRvX8AMaGcAQg5o4ON8e6LZrnl6Senp4TPva///u/VV1drfPPP19f+tKXNHnyZKWlpWndunVjHrkZ7mtMS0sb0/MAiC7CEYCo27dvn0466aQBP1uWpRkzZsi2bVmWpX379qmsrCx8n+bmZrW3t6u4uHjQuWzbHhA09u7dK0nh++bk5EiS2tvbw/8tSQcOHDhhW1944QWddNJJuvfeewc8x9133z3gfiMJc8XFxSN6jQCSCzVHAKLuySefHPDzE088IUk655xzdO6550qSfvrTnw64z6OPPipJ4eN9Dh48qD/84Q/hnzs7O/Xss89q3rx54UtqfZevXn/99fD9vF7vsKbD943a2LYdvm379u164403BtzP4/FI6g1gJzLS1wgguTByBCDq3nvvPX3lK1/Rhz/8Yb3xxhv6zW9+oxUrVmju3LmSpH/5l3/RL3/5S7W3t+vMM8/Um2++qWeeeUbnn3/+gFlcUm990be//W29+eabmjx5sp566ikdPnxYt912W/g+S5Ys0fTp0/Xtb39b9fX1SktL01NPPaW8vLwTjh4tW7ZMv//977V69WotW7ZM7733nn7xi19ozpw58nq94fulp6drzpw5eu6551RSUqJJkybpQx/6kE4++eRB55w7d+6IXiOA5EI4AhB1P/7xj3XXXXfpjjvukMPh0MqVK/Xv//7v4eO33nqrZsyYoWeeeUb/8z//o4KCAq1atUpr1qwZdK6SkhLdeOONuv3227Vnzx7NmDFDd955pz784Q+H7+N0OnXvvffqlltu0V133aUpU6boc5/7nHJycvTNb37zuG295JJL1NzcrF/+8pfatGmT5syZox/+8Id6/vnnB20Vcuutt+r//t//q9tuu03BYFBr1qwZMhyN9DUCSC6GHTmWDABjcM899+jee+/Vli1blJ+fP+bzVVVV6UMf+pDWrVsXhdYBwPBQcwQAABCBcAQAABCBcAQAABCBmiMAAIAIjBwBAABEIBwBAABEIBwBAABEIBwBAABEIBwBAABEIBwBAABEIBwBAABEIBwBAABEIBwBAABE+P8BTN8BOk/9YrcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设 df 是你的数据框\n",
    "# df = ...\n",
    "\n",
    "\n",
    "sns.lmplot(data=df, x='population', y='profit', height=6, fit_reg=False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GPFqxv_zU8eJ"
   },
   "source": [
    "多变量的假设 h 表示为：${{h}_{\\theta }}\\left( x \\right)={{\\theta }_{0}}+{{\\theta }_{1}}{{x}_{1}}+{{\\theta }_{2}}{{x}_{2}}+...+{{\\theta }_{n}}{{x}_{n}}$。\n",
    "\n",
    "这个公式中有n+1个参数和n个变量，为了使得公式能够简化一些，引入${{x}_{0}}=1$，则公式转化为：  ${{h}_{\\theta }}\\left( x \\right)={{\\theta }_{0}x_0}+{{\\theta }_{1}}{{x}_{1}}+{{\\theta }_{2}}{{x}_{2}}+...+{{\\theta }_{n}}{{x}_{n}}$。\n",
    "\n",
    "此时模型中的参数是一个n+1维的向量，任何一个训练实例也都是n+1维的向量，特征矩阵X的维度是 m*(n+1)。 因此公式可以简化为：${{h}_{\\theta }}\\left( x \\right)={{\\theta }^{T}}X$，其中上标T代表矩阵转置。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "on8_khfsU8eQ"
   },
   "source": [
    "# 计算代价函数\n",
    "$$J\\left( \\theta  \\right)=\\frac{1}{2m}\\sum\\limits_{i=1}^{m}{{{\\left( {{h}_{\\theta }}\\left( {{x}^{(i)}} \\right)-{{y}^{(i)}} \\right)}^{2}}}$$\n",
    "\n",
    "其中：\n",
    "\n",
    "$${{h}_{\\theta }}\\left( x \\right)={{\\theta }^{T}}X={{\\theta }_{0}}{{x}_{0}}+{{\\theta }_{1}}{{x}_{1}}+{{\\theta }_{2}}{{x}_{2}}+...+{{\\theta }_{n}}{{x}_{n}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yomFBPelU8eQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(97, 2) <class 'numpy.ndarray'>\n",
      "(97,) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "def get_X(df):\n",
    "    ones = pd.DataFrame({'ones': np.ones(len(df))})  # ones是m行1列的dataframe\n",
    "    data = pd.concat([ones, df], axis=1)  # 合并数据，根据列合并\n",
    "    return data.iloc[:, :-1].values  # 使用values属性获取数据并返回numpy数组形式的特征矩阵\n",
    "\n",
    "\n",
    "# 假设这里是用于获取目标变量y的函数，原代码中没有给出具体实现，以下是简单示例，你可以按实际情况调整\n",
    "def get_y(df):\n",
    "    return df.iloc[:, -1].values  # 同样使用values属性获取目标变量数据并返回numpy数组形式\n",
    "\n",
    "# 查看数据维度\n",
    "data = df\n",
    "X = get_X(data)\n",
    "print(X.shape, type(X))\n",
    "\n",
    "y = get_y(data)\n",
    "print(y.shape, type(y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AeTfJ6UyU8eT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0.]\n"
     ]
    }
   ],
   "source": [
    "theta = np.zeros(X.shape[1]) # X.shape[1]=2, 代表特征数n\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ntnGgUoKU8eV"
   },
   "outputs": [],
   "source": [
    "def lr_cost(theta, X, y):\n",
    "    \"\"\" 计算代价函数\n",
    "    X: R(m*n), m 样本数, n 特征数\n",
    "    y: R(m)\n",
    "    theta : R(n), 线性回归的参数\n",
    "    \"\"\"\n",
    "    m = X.shape[0]#m为样本数\n",
    "\n",
    "    inner = X @ theta - y  # R(m*1)，X @ theta等价于X.dot(theta)\n",
    "\n",
    "    # 1*m @ m*1 = 1*1 in matrix multiplication\n",
    "    # but you know numpy didn't do transpose in 1d array, so here is just a\n",
    "    # vector inner product to itselves\n",
    "    square_sum = inner.T @ inner\n",
    "    cost = square_sum / (2 * m)\n",
    "\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BPFR1bKOU8eY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.072733877455676"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_cost(theta, X, y) # 返回cost的值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KZ9oOCHrU8eb"
   },
   "source": [
    "# 批量梯度下降 - Batch Gradient Decent\n",
    "$$\\begin{aligned}{{\\theta }_{j}} &:={{\\theta }_{j}}-\\alpha \\frac{\\partial }{\\partial {{\\theta }_{j}}}J\\left( \\theta  \\right) \\\\ &:= {{\\theta }_{j}}-\\alpha \\frac{1}{m} \\sum^{m}_{i=1}\\left( h_\\theta \\left(x^{(i)}\\right) -y^{(i)}  \\right)x^{(i)}_j \\end{aligned}$$\n",
    "注意：对于所有的$j$，需要同时更新$\\theta_j$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4_JZqMY3U8ec"
   },
   "outputs": [],
   "source": [
    "def gradient(theta, X, y):\n",
    "  \"\"\"\n",
    "  计算梯度，也就是 J(θ)的偏导数\n",
    "  \"\"\"\n",
    "  m = X.shape[0]\n",
    "\n",
    "  inner = X.T @ (X @ theta - y)  # (m,n).T @ (m, 1) -> (n, 1)，X @ theta等价于X.dot(theta)\n",
    "\n",
    "  return inner / m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IRexXn6EU8ee"
   },
   "outputs": [],
   "source": [
    "def batch_gradient_decent(theta, X, y, epoch, alpha=0.01):\n",
    "  \"\"\"\n",
    "  批量梯度下降函数。拟合线性回归，返回参数和代价\n",
    "    epoch: 批处理的轮数\n",
    "  \"\"\"\n",
    "  cost_data = [lr_cost(theta, X, y)]\n",
    "  _theta = theta.copy()  # 拷贝一份，不和原来的theta混淆\n",
    "\n",
    "  for _ in range(epoch):\n",
    "    _theta = _theta - alpha * gradient(_theta, X, y)\n",
    "    cost_data.append(lr_cost(_theta, X, y))\n",
    "\n",
    "  return _theta, cost_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lx_dnNrxU8ei"
   },
   "outputs": [],
   "source": [
    "epoch = 500\n",
    "final_theta, cost_data = batch_gradient_decent(theta, X, y, epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kCe-db1AU8en"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.28286727,  1.03099898])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_theta\n",
    "#最终的theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yigjBbL4U8eq"
   },
   "outputs": [
    {
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       " 4.869480312344594,\n",
       " 4.868066517807122,\n",
       " 4.866657815675986,\n",
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       " 4.863855615328574,\n",
       " 4.862462080625159,\n",
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       " 4.859690051433371,\n",
       " 4.858311520850715,\n",
       " 4.856937955655664,\n",
       " 4.8555693379631775,\n",
       " 4.85420564995264,\n",
       " 4.852846873867617,\n",
       " 4.851492992015639,\n",
       " 4.850143986767961,\n",
       " 4.8487998405593355,\n",
       " 4.847460535887784,\n",
       " 4.846126055314369,\n",
       " 4.844796381462968,\n",
       " 4.843471497020046,\n",
       " 4.842151384734428,\n",
       " 4.840836027417081,\n",
       " 4.839525407940883,\n",
       " 4.838219509240405,\n",
       " 4.836918314311682,\n",
       " 4.835621806212003,\n",
       " 4.834329968059677,\n",
       " 4.833042783033826,\n",
       " 4.831760234374157,\n",
       " 4.830482305380744,\n",
       " 4.829208979413817,\n",
       " 4.8279402398935405,\n",
       " 4.826676070299798,\n",
       " 4.825416454171979,\n",
       " 4.824161375108761,\n",
       " 4.822910816767899,\n",
       " 4.821664762866012,\n",
       " 4.820423197178371,\n",
       " 4.819186103538688,\n",
       " 4.817953465838902,\n",
       " 4.816725268028978,\n",
       " 4.815501494116686,\n",
       " 4.8142821281674015,\n",
       " 4.813067154303901,\n",
       " 4.811856556706141,\n",
       " 4.810650319611066,\n",
       " 4.809448427312395,\n",
       " 4.808250864160424,\n",
       " 4.807057614561818,\n",
       " 4.805868662979403,\n",
       " 4.804683993931976,\n",
       " 4.80350359199409,\n",
       " 4.802327441795866,\n",
       " 4.80115552802278,\n",
       " 4.799987835415476,\n",
       " 4.798824348769555,\n",
       " 4.797665052935391,\n",
       " 4.796509932817918,\n",
       " 4.795358973376449,\n",
       " 4.7942121596244665,\n",
       " 4.793069476629437,\n",
       " 4.791930909512612,\n",
       " 4.790796443448837,\n",
       " 4.789666063666354,\n",
       " 4.788539755446615,\n",
       " 4.787417504124084,\n",
       " 4.786299295086054,\n",
       " 4.785185113772448,\n",
       " 4.784074945675635,\n",
       " 4.7829687763402395,\n",
       " 4.781866591362953,\n",
       " 4.78076837639235,\n",
       " 4.779674117128695,\n",
       " 4.778583799323759,\n",
       " 4.777497408780635,\n",
       " 4.776414931353551,\n",
       " 4.775336352947692,\n",
       " 4.774261659519007,\n",
       " 4.773190837074031,\n",
       " 4.772123871669708,\n",
       " 4.771060749413197,\n",
       " 4.770001456461701,\n",
       " 4.768945979022287,\n",
       " 4.767894303351698,\n",
       " 4.766846415756183,\n",
       " 4.765802302591315,\n",
       " 4.764761950261813,\n",
       " 4.763725345221363,\n",
       " 4.762692473972447,\n",
       " 4.761663323066164,\n",
       " 4.7606378791020525,\n",
       " 4.7596161287279255,\n",
       " 4.75859805863968,\n",
       " 4.757583655581141,\n",
       " 4.7565729063438775,\n",
       " 4.755565797767038,\n",
       " 4.7545623167371724,\n",
       " 4.753562450188067,\n",
       " 4.752566185100569,\n",
       " 4.751573508502424,\n",
       " 4.750584407468098,\n",
       " 4.749598869118619,\n",
       " 4.7486168806214,\n",
       " 4.7476384291900775,\n",
       " 4.7466635020843455,\n",
       " 4.745692086609785,\n",
       " 4.744724170117706,\n",
       " 4.743759740004973,\n",
       " 4.742798783713851,\n",
       " 4.741841288731832,\n",
       " 4.740887242591484,\n",
       " 4.739936632870274,\n",
       " 4.738989447190422,\n",
       " 4.7380456732187275,\n",
       " 4.737105298666416,\n",
       " 4.736168311288972,\n",
       " 4.735234698885989,\n",
       " 4.734304449301005,\n",
       " 4.733377550421341,\n",
       " 4.732453990177952,\n",
       " 4.731533756545262,\n",
       " 4.73061683754101,\n",
       " 4.729703221226098,\n",
       " 4.7287928957044265,\n",
       " 4.727885849122751,\n",
       " 4.726982069670519,\n",
       " 4.726081545579717,\n",
       " 4.725184265124722,\n",
       " 4.724290216622143,\n",
       " 4.7233993884306775,\n",
       " 4.722511768950947,\n",
       " 4.721627346625359,\n",
       " 4.7207461099379495,\n",
       " 4.7198680474142325,\n",
       " 4.718993147621053,\n",
       " 4.71812139916644,\n",
       " 4.717252790699452,\n",
       " 4.7163873109100365,\n",
       " 4.715524948528874,\n",
       " 4.7146656923272445,\n",
       " 4.713809531116866]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_data\n",
    "# 看下代价数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZrMX8THHU8et"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.713809531116866"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算最终的代价\n",
    "lr_cost(final_theta, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B9VOLMPqk2Os"
   },
   "source": [
    "scikit-learn model的预测表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5gM2jYk2T0Hv",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "x = X[:, 1]\n",
    "f = model.predict(X).flatten()\n",
    "\n",
    "plt.scatter(X[:,1], y, label='Traning Data')\n",
    "plt.plot(x, f, 'r', label='Prediction')\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rsQw66Y9U8ew"
   },
   "source": [
    "# 代价数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YZyEbK4RU8ew"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000000e-05 3.00000000e-05 2.15443469e-04 6.46330407e-04\n",
      " 4.64158883e-03 1.39247665e-02 1.00000000e-01 3.00000000e-01]\n"
     ]
    }
   ],
   "source": [
    "base = np.logspace(-1, -5, num=4)\n",
    "candidate = np.sort(np.concatenate((base, base*3)))\n",
    "print(candidate)\n",
    "#可以看到从第二轮代价数据变换很大，接下来平稳了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5XhEi6SqU8ez"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = final_theta[0] # intercept，Y轴上的截距\n",
    "m = final_theta[1] # slope，斜率\n",
    "\n",
    "plt.scatter(data.population, data.profit, label=\"Training data\")\n",
    "plt.plot(data.population, data.population*m + b, 'r', label=\"Prediction\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "67DO0QyBU8e2"
   },
   "source": [
    "# 3- 选修章节"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tb-CO9raU8e2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>square</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2104</td>\n",
       "      <td>3</td>\n",
       "      <td>399900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "      <td>329900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>369000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1416</td>\n",
       "      <td>2</td>\n",
       "      <td>232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>539900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   square  bedrooms   price\n",
       "0    2104         3  399900\n",
       "1    1600         3  329900\n",
       "2    2400         3  369000\n",
       "3    1416         2  232000\n",
       "4    3000         4  539900"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('ex1data2.txt', names=['square', 'bedrooms', 'price'])\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7WBXHMXzU8e6"
   },
   "source": [
    "# 标准化数据\n",
    "最简单的方法是令：\n",
    "\n",
    " \n",
    "\n",
    "其中  是平均值，sn 是标准差。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sFLN1gSfU8e7"
   },
   "outputs": [],
   "source": [
    "def normalize_feature(df):\n",
    "#     \"\"\"Applies function along input axis(default 0) of DataFrame.\"\"\"\n",
    "    return df.apply(lambda column: (column - column.mean()) / column.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SL2V8gRmU8e-"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>square</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.130010</td>\n",
       "      <td>-0.223675</td>\n",
       "      <td>0.475747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.504190</td>\n",
       "      <td>-0.223675</td>\n",
       "      <td>-0.084074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.502476</td>\n",
       "      <td>-0.223675</td>\n",
       "      <td>0.228626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.735723</td>\n",
       "      <td>-1.537767</td>\n",
       "      <td>-0.867025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.257476</td>\n",
       "      <td>1.090417</td>\n",
       "      <td>1.595389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     square  bedrooms     price\n",
       "0  0.130010 -0.223675  0.475747\n",
       "1 -0.504190 -0.223675 -0.084074\n",
       "2  0.502476 -0.223675  0.228626\n",
       "3 -0.735723 -1.537767 -0.867025\n",
       "4  1.257476  1.090417  1.595389"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = normalize_feature(raw_data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pjP2SsV3U8fE"
   },
   "source": [
    "# 2. 多变量批量梯度下降 -  Multi-var batch gradient decent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XHTA9KcXU8fE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 3) <class 'numpy.ndarray'>\n",
      "(47,) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X = get_X(data)\n",
    "print(X.shape, type(X))\n",
    "\n",
    "y = get_y(data)\n",
    "print(y.shape, type(y)) #看下数据的维度和类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PUHGSR5jU8fI"
   },
   "outputs": [],
   "source": [
    "alpha = 0.01 #学习率\n",
    "theta = np.zeros(X.shape[1]) #X.shape[1]：特征数n\n",
    "epoch = 500  #迭代次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NbC5-6NfU8fL"
   },
   "outputs": [],
   "source": [
    "final_theta, cost_data = batch_gradient_decent(theta, X, y, epoch, alpha=alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DTvLgpImU8fN"
   },
   "outputs": [],
   "source": [
    "# 正规方程\n",
    "def normalEqn(X, y):\n",
    "    theta = np.linalg.inv(X.T@X)@X.T@y#X.T@X等价于X.T.dot(X)\n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XIAeC3aWU8fQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.18002534e-16,  8.30383883e-01,  8.23982853e-04])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xhv7H1OSTZJs"
   },
   "source": [
    "Scikit-learn 的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dx3gtarBk2Os",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn import linear_model\n",
    "\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "f = model.predict(X).flatten()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(X[:,1], X[:,2], f, 'r', label='Prediction')\n",
    "ax.scatter(X[:,1], X[:,2], y, label='Traning Data')\n",
    "ax.legend(loc=2)\n",
    "ax.set_xlabel('square')\n",
    "ax.set_ylabel('bedrooms')\n",
    "ax.set_zlabel('price')\n",
    "ax.set_title('square & bedrooms vs. price')\n",
    "#ax.view_init(30, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IRrnttEHU8fS"
   },
   "source": [
    "# 3. 学习率 - Learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w9U-3YXMU8fT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000000e-05 3.00000000e-05 2.15443469e-04 6.46330407e-04\n",
      " 4.64158883e-03 1.39247665e-02 1.00000000e-01 3.00000000e-01]\n"
     ]
    }
   ],
   "source": [
    "base = np.logspace(-1, -5, num=4)\n",
    "candidate = np.sort(np.concatenate((base, base*3)))\n",
    "print(candidate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ePELIHY-U8fV"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch=50\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "for alpha in candidate:\n",
    "    _, cost_data = batch_gradient_decent(theta, X, y, epoch, alpha=alpha)\n",
    "    ax.plot(np.arange(epoch+1), cost_data, label=alpha)\n",
    "\n",
    "ax.set_xlabel('epoch', fontsize=12)\n",
    "ax.set_ylabel('cost', fontsize=12)\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "ax.set_title('learning rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SwpPCazZg3Ik"
   },
   "source": [
    "可以看到最合适的learning rate是0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6-HlM4onU8fX"
   },
   "source": [
    "# 4. 正规方程 - Normal equation\n",
    "正规方程是通过求解下面的方程来找出使得代价函数最小的参数的：$\\frac{\\partial }{\\partial {{\\theta }_{j}}}J\\left( {{\\theta }_{j}} \\right)=0$ 。\n",
    " 假设我们的训练集特征矩阵为 X（包含了${{x}_{0}}=1$）并且我们的训练集结果为向量 y，则利用正规方程解出向量 $\\theta ={{\\left( {{X}^{T}}X \\right)}^{-1}}{{X}^{T}}y$ 。\n",
    "上标T代表矩阵转置，上标-1 代表矩阵的逆。设矩阵$A={{X}^{T}}X$，则：${{\\left( {{X}^{T}}X \\right)}^{-1}}={{A}^{-1}}$\n",
    "\n",
    "梯度下降与正规方程的比较：\n",
    "\n",
    "梯度下降：需要选择学习率α，需要多次迭代，当特征数量n大时也能较好适用，适用于各种类型的模型\t\n",
    "\n",
    "正规方程：不需要选择学习率α，一次计算得出，需要计算${{\\left( {{X}^{T}}X \\right)}^{-1}}$，如果特征数量n较大则运算代价大，因为矩阵逆的计算时间复杂度为O(n3)，通常来说当n小于10000 时还是可以接受的，只适用于线性模型，不适合逻辑回归模型等其他模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BGjQLe7jU8fY"
   },
   "outputs": [],
   "source": [
    "# 正规方程\n",
    "def normalEqn(X, y):\n",
    "    theta = np.linalg.inv(X.T@X)@X.T@y#X.T@X等价于X.T.dot(X)\n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3JB8AH_iU8fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.11022302e-16,  8.84765988e-01, -5.31788197e-02])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_theta2=normalEqn(X, y)#感觉和批量梯度下降的theta的值有点差距\n",
    "final_theta2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YfBLlOZnY6Qi"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = final_theta2[0] + final_theta2[1] * X[:,1] + final_theta2[2] * X[:,2]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(X[:,1], X[:,2], f, 'r', label='Prediction')\n",
    "ax.scatter(X[:,1], X[:,2], y, label='Traning Data')\n",
    "ax.legend(loc=2)\n",
    "ax.set_xlabel('square')\n",
    "ax.set_ylabel('bedrooms')\n",
    "ax.set_zlabel('price')\n",
    "ax.set_title('square & bedrooms vs. price')\n",
    "#ax.view_init(30, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "1.linear_regreesion_v1.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
